A machine learning system comprises a learning planning part 81 configured to create a learning plan of a neural network model, a data set creating part 82 configured to create training data sets, a learning part 83 configured to perform learning of the neural network model using the training data sets when the vehicle is stopped, and a stopping period predicting part 84 configured to predict stopping periods of the vehicle. The data set creating part is configured to allocate the training data sets to a plurality of learning regions determined based on respective ranges of the plurality of input parameters. The learning part is configured to perform learning of the neural network model for each learning region. The learning planning part is configured to allocate the plurality of learning regions to the stopping periods so that learning of the neural network model is not suspended during the stopping periods.
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3. The machine learning system according to claim 1, wherein priority orders of learning are given to the plurality of learning regions.
4. The machine learning system according to claim 3, wherein the learning planning part is configured to make the priority orders of learning of learning regions including values of the input parameters with relatively high frequencies of appearance higher than the priority orders of learning of learning regions including values of the input parameters with relatively low frequencies of appearance.
5. The machine learning system according to claim 3, wherein the learning planning part is configured to change the priority orders of learning in accordance with information on a road over which the vehicle is being driven or a warmup state of an internal combustion engine provided in the vehicle.
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December 30, 2019
September 13, 2022
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